Bias and misalignment compensation for 6-dof imu using gnss/ins data

ABSTRACT

A system and method for correcting bias and angle misalignment errors in the angle rate and acceleration outputs from a 6-DOF IMU mounted to a vehicle. The method includes providing velocity and estimation attitude data in an inertial frame from, for example, a GNSS/INS, and determining an ideal acceleration estimation and an ideal rate estimation in a vehicle frame using the velocity and attitude data. The method then determines the IMU bias error and misalignment error using the ideal acceleration and rate estimations and the angle rate and acceleration outputs in an IMU body frame from the IMU.

BACKGROUND OF THE INVENTION

Field of the Invention

This invention relates generally to system and method for determiningbias and misalignment errors in the outputs from a 6-degree of freedom(DOF) inertial measurement unit (IMU) and, more particularly, to asystem and method for determining bias and misalignment errors in theoutputs from a 6-DOF IMU on a vehicle using velocity and attitude datafrom a global navigation satellite system (GNSS) and/or an inertialnavigation system (INS), where the method includes determining an idealacceleration estimation and an ideal angle rate estimation in a vehicleframe using the velocity and attitude data, and determining an IMU biaserror and a misalignment error using the ideal acceleration and angularrate estimation and the measured angle rate and acceleration outputsfrom the IMU.

Discussion of the Related Art

The operation of modern vehicles is becoming more autonomous, i.e.,vehicles are able to provide driving control with less driverintervention. Cruise control systems have been on vehicles for a numberof years where the vehicle operator can set a particular speed of thevehicle, and the vehicle will maintain that speed without the driveroperating the throttle. Adaptive cruise control systems have beenrecently developed in the art where not only does the system maintainthe set speed, but also will automatically slow the vehicle down in theevent that a slower moving preceding vehicle is detected using varioussensors, such as radar and cameras. Certain modern vehicles also provideautonomous parking where the vehicle will automatically provide thesteering control for parking the vehicle. Some vehicle systems providingautomatic braking without driver intervention to avoid rear-endcollisions. As vehicle systems improve, they will become more autonomouswith the goal being a completely autonomously driven vehicle. Forexample, future vehicles probably will employ autonomous systems forlane changing, passing, turns away from traffic, turns into traffic,etc.

Various active safety control systems, driver assist systems andautonomous driving operations on vehicles, such as systems providingelectronic stability control (ECS), adaptive cruise control (ACC), lanekeeping (LK), lane changing (LC), etc., require highly robust andprecise modules for estimating various vehicle dynamics. Such modulesare necessary to provide knowledge of the vehicle position and velocityto control the vehicle.

Active safety control for the systems discussed above, and others, relyon accurate vehicle speed estimation for proper performance. Currently,these types of proposed systems rely on wheel speed sensors and othervehicle kinematic inputs to provide the vehicle speed estimation.However, sometimes sensors and control modules that determine vehiclespeed fail or operate improperly, where loss of vehicle speed could beserious. Certain automotive safety requirements, such as ASIL-D, requireredundant vehicle speed estimation processes in the event the primaryestimation processor fails. For example, for those systems that requireactive control, the control systems are required to provide accuratespeed estimation for five seconds after the failure event so as to givethe driver time to take control of the vehicle.

It is known in the art to provide a 6-DOF IMU on a vehicle that providessix rate of change measurements, particularly, angular rate measurementsof vehicle roll, pitch and yaw, and acceleration measurements oflongitudinal acceleration, lateral acceleration and up/downacceleration. A typical IMU provided on a vehicle will include threegyros that provide the angular rate measurements and threeaccelerometers that provide the acceleration measurements.

It has been proposed in the art to use the angular rate and accelerationmeasurements from an IMU on a vehicle to provide signals that can beused for vehicle speed estimation. U.S. patent application Ser. No.14/680,894, filed Apr. 7, 2015, titled, Fail Operational Vehicle SpeedEstimation Through Data Fusion of 6-DOF, IMU, GPS and Radar, discloses asystem and method for providing redundant vehicle speed estimation thatdetermines whether one or more primary sensors that provide vehiclespeed has failed, and if so, estimates the vehicle speed in a secondarymodule using a buffered vehicle speed value and inertial measurementsignals from an IMU. The method also uses GPS signal data and/orvelocity data provided by range sensors from static objects to improvethe estimated vehicle speed if they are available.

In the process discussed in the '894 application, the accuracy of thesecondary vehicle speed estimation largely depends on the accuracy ofthe IMU acceleration and angular rate measurements. However, themeasurements of acceleration and angular rates provided by the IMU on avehicle for this purpose usually deviate from the actual accelerationand angular rate values due to several error sources. The two majorsources of error for the IMU measurements are accelerometer and gyrobias components and frame misalignment corresponding to mounting the IMUto the vehicle. There is a bias term on each axis, which corresponds tosix bias error terms in addition to three misalignment errors. Althoughit is desirable to perfectly align the IMU with the vehicle frame, therestill are mounting errors that even if they are small, could providesignificant misalignment errors in the calculations. Further, the biasand misalignment errors can be improved during manufacture of thevehicle by moving the vehicle and identifying the errors. However, sucherror corrections of bias and misalignment are also difficult toaccurately obtain. Further, these error corrections for bias andmisalignment often require special vehicle maneuvers during themanufacturing stage of the vehicle, and further bias terms are not fixedand may change in time due to different road, vehicle and environmentconditions.

SUMMARY OF THE INVENTION

The present disclosure describes a system and method for correcting biasand angle misalignment errors in the angle rate and acceleration outputsfrom a 6-DOF IMU mounted to a vehicle. The method includes providingaccurate velocity and estimation attitude data in an inertial framefrom, for example, a GNSS/INS, and determining an ideal accelerationestimation and an ideal rate estimation in a vehicle frame using thevelocity and attitude data. The method then determines the IMU biaserror and misalignment error using the ideal acceleration and rateestimations and the angle rate and acceleration outputs in an IMU bodyframe from the IMU.

Additional features of the present invention will become apparent fromthe following description and appended claims, taken in conjunction withthe accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an isometric illustration of a vehicle showing a vehiclereference frame and an IMU body frame;

FIG. 2 is a block diagram of an IMU error estimation system; and

FIG. 3 is a flow diagram showing a process for providing IMU errorestimations.

DETAILED DESCRIPTION OF THE EMBODIMENTS

The following discussion of the embodiments of the invention directed toa system and method for identifying IMU bias and misalignment errors ina 6-DOF IMU on a vehicle is merely exemplary in nature, and is in no wayintended to limit the invention or its applications or uses. Forexample, the system and method has particular application for secondaryvehicle speed estimation. However, as will be appreciated by thoseskilled in the art, the system and method may have other applicationsand for other mobile platforms, such as on trains, machines, tractors,boats, recreation vehicles, etc.

FIG. 1 is an isometric view of a vehicle 10 including a GNSS/INS unit 12that provides accurate vehicle velocity and attitude data includingroll, yaw and pitch angles in a known manner. Further, the vehicle 10includes sensors 14 intended to represent one or more of wheel speedsensors, steering wheel angle sensors, yaw rate sensors, longitudinaland lateral acceleration sensors, etc. as required by the discussionherein. The vehicle 10 also includes a 6-DOF IMU 16 that provides theacceleration and angular rate measurements as also discussed herein. Asmentioned, the IMU 16 is a known device that includes threeaccelerometers and three gyros. When the vehicle speed measurement datais unavailable, the measurements from the IMU 16 can be employed toprovide vehicle speed estimations as discussed, for example, in the '894application.

The discussion herein will refer to three coordinate frames including aninertial frame (N-frame), which is a fixed reference frame on the groundwith the X-axis directed to the east, the Y-axis directed to the northand the Z-axis directed upward. A vehicle frame 20 (V-frame) is abody-fixed frame located at the center of gravity of the vehicle 10 withthe X-axis directed forward, the Y-axis directed to the left and theZ-axis directed upward, as shown in FIG. 1. A body frame 22 (b-frame) isa body-fixed frame located at the center of the IMU 16, where the X-Y-Zaxes are aligned with the X-Y-Z axes of the accelerometers and gyros inthe IMU 16. The IMU errors include the six components of bias for eachaccelerometer and gyro along the X-Y-Z axes of the body frame 22 andthree misalignment angles corresponding to the rotation angles betweenthe body frame 22 and the vehicle frame 20. For each axis of the bodyframe 22, there are two bias terms, specifically, one for theacceleration and one for the angular rate.

FIG. 2 is a block diagram of a system 30 that employs an IMU errorestimation model to correct for the bias and misalignment errorsreferred to above. The system 30 includes an IMU error estimation module32 that estimates the IMU bias and misalignment errors and a stateestimation module 34 that corrects the raw measurements from the IMU 16and utilizes them in the velocity and attitude estimation models. In theerror estimation module 32, the accurate vehicle velocity and attitudeare provided at box 36 using, for example, the GNSS/INS data. Using thevehicle velocity and attitude data from the box 36, the module 32determines an ideal acceleration and angular rate in the vehicle frame20 at box 38.

The discussion below details how the ideal acceleration and angular rateare determined. The following equations define the velocities and anglerates in the vehicle frame 20.

{dot over (V)} ^(v)=−Ω^(v) V ^(v) +R _(n) ^(v) g ^(n) +f ^(v),  (1)

{dot over (R)} _(n) ^(v)=−Ω^(v) R _(n) ^(v),  (2)

where {dot over (V)}^(v) is the time derivate of the vehicle velocity inthe vehicle frame 20, g^(n)=[0,0,−9.80665]^(t) is the vector ofgravitation acceleration in the inertial frame, f^(v) is theacceleration vector in the vehicle frame 20, and R_(n) ^(v) is atransformation matrix from the inertial frame to the vehicle frame 20corresponding to the Euler angle rotations of roll φ, pitch θ and yaw ψ,where:

$\begin{matrix}{R_{n}^{v} = {\quad{\begin{bmatrix}{\cos \; {\theta cos\psi}} & {\cos \; {\theta sin\psi}} & {{- \sin}\; \theta} \\{{\cos \; {\psi sin\varphi sin}\; \theta} - {\cos \; {\varphi sin}\; \psi}} & {{\sin \; {\varphi sin}\; {\theta sin}\; \psi} + {\cos \; \varphi \; \cos \; \psi}} & {\cos \; {\theta sin\varphi}} \\{{\cos \; {\psi cos\varphi cos\theta}} + {\sin \; {\varphi sin\psi}}} & {{\cos \; {\varphi sin}\; {\theta sin\psi}} - {\sin \; {\varphi cos}\; \psi}} & {\cos \; {\theta cos}\; \varphi}\end{bmatrix},}}} & (3)\end{matrix}$

and where Ω^(v)=[(ω^(v)x] is the skew-symmetric matrix of the IMU'svehicle angular rates ω^(v) as:

$\begin{matrix}{\Omega^{v} = {\begin{bmatrix}0 & {- \omega_{\psi}} & \omega_{\theta} \\\omega_{\psi} & 0 & {- \omega_{\varphi}} \\{- \omega_{\theta}} & \omega_{\varphi} & 0\end{bmatrix}.}} & (4)\end{matrix}$

Assuming that the vehicle velocity V^(v) and the roll angle φ, pitchangle θ and yaw angle ψ are known from the GNSS/INS data, by usingequation (1) the ideal IMU acceleration vector f^(v) and the angularrate measurements Ω^(v) in the vehicle frame 20 can be found as:

Ω^(v) =−{dot over (R)} ^(v)(R _(n) ^(v))^(T),  (5)

f ^(v) ={dot over (V)} ^(v)+Ω^(v) V ^(v) −R _(n) ^(v) g ^(n),  (6)

where {dot over (V)}^(v) is obtained using the numerical derivatives ofthe velocity data and {dot over (R)}_(n) ^(v) is obtained using equation(3) as:

$\begin{matrix}{{{\overset{.}{R}}_{n}^{v} = {{\frac{\partial R_{n}^{v}}{\varphi}\overset{.}{\varphi}} + {\frac{\partial R_{n}^{v}}{\theta}\overset{.}{\theta}} + {\frac{R_{n}^{v}}{\psi}\overset{.}{\psi}}}},} & (7)\end{matrix}$

where {dot over (φ)}, {dot over (θ)} and {dot over (ψ)} are calculatednumerically.

The ideal IMU acceleration vector f^(v) and the ideal angular rate ω^(v)from the box 38 are sent to an IMU bias and misalignment estimation box40 that compares the ideal acceleration vector r and the ideal angularrate Ω^(v) estimation values to the angle rates cob in the body frame 22from the gyros in the IMU 16 provided at box 42 and the accelerationvalues f^(b) in the body frame 22 from the accelerometers in the IMU 16provided at box 44 to calculate the bias and misalignment estimationerrors. As long as the GNSS/INS date provides accurate information todetermine the vehicle velocity and attitude at the box 36, then the biasand misalignment errors for each accelerometer on the X-Y-Z axes of thebody frame 22 and the three misalignment angle errors can be accuratelyobtained at the box 40.

In order to calculate the bias and misalignment estimation errors, arelationship between the ideal and the actual measurements is obtained.The IMU measurements in equation (5) are ideal measurements in the sensethat they do not include any IMU errors. Considering bias andmisalignment as two significant errors, the ideal values and the actualIMU measurements are related as:

f ^(b) =R _(v) ^(b) f ^(v) =f _(bias),  (8)

ω^(b) =R _(v) ^(b)ω^(v)+ω_(bias),  (9)

where f^(b) and ω^(b) are the actual IMU measurements in the body frame22, f_(bias) and ω_(bias) are the bias vectors of the accelerometers andgyros, respectively, and R_(v) ^(b) is the transformation matrix fromthe vehicle frame 20 to the body frame 22 corresponding to the IMUmisalignment angles as:

$\begin{matrix}{R_{v}^{b} = {\quad{\begin{bmatrix}{\cos \; {\beta cos}\; \gamma} & {\cos \; {\beta sin}\; \gamma} & {{- \sin}\; \beta} \\{{\cos \; {\gamma sin\alpha sin\beta}} - {\cos \; {\alpha sin\gamma}}} & {{\sin \; {\gamma sin}\; {\alpha sin\beta}} + {\cos \; {\alpha cos}\; \gamma}} & {\cos \; {\beta sin\alpha}} \\{{\cos \; {\gamma cos\alpha sin\beta}} + {\sin \; {\alpha sin\gamma}}} & {{\sin \; {\gamma cos}\; {\alpha sin}\; \beta} - {\sin \; {\alpha cos}\; \gamma}} & {\cos \; {\beta cos}\; \alpha}\end{bmatrix},}}} & (10)\end{matrix}$

where α, β and γ are the misalignment Euler angles about the X, Y and Zaxes, respectively.

Equations (8) and (9) include six equations with nine unknowns to befound. To find a unique solution to the equations, three equationsrelated to the acceleration measurements from a different time instantare necessary.

In the box 40, the three misalignment angles α, β and γ are first foundusing the ideal acceleration measurements at two different time instantsin the following two cases. The first case varies the longitudinalacceleration with constant lateral and vertical accelerations. In thiscase, the following conditions hold for the ideal accelerationmeasurements of the IMU 16 at times t₁ and t₂ as:

f _(c) ^(v)(t ₁)≠f _(x) ^(v)(t ₂),  (11)

f _(y) ^(v)(t ₁)=f _(y) ^(v)(t ₂),  (12)

f _(z) ^(v)(t ₁)=f _(z) ^(v)(t ₂).  (13)

Assuming that the bias terms remain constant at times t₁ and t₂ andusing equations (8)-(13), the difference between the actual IMUmeasurements at times t₁ and t₂ is found as:

$\begin{matrix}{\quad\{ {\begin{matrix}{{\Delta \; f_{x}^{b}} = {\cos \; {\beta cos}\; \Delta \; f_{x}^{v}}} \\{{\Delta \; f_{b}^{y}} = {( {{\cos \; {\gamma sin\alpha sin\beta}} - {\cos \; {\alpha sin}\; \gamma}} )\Delta \; f_{x}^{v}}} \\{{\Delta \; f_{z}^{b}} = {( {{\cos \; {\gamma cos}\; {\alpha sin}\; \beta} + {\sin \; {\alpha sin}\; \gamma}} )\Delta \; f_{x}^{v}}}\end{matrix},} } & (14)\end{matrix}$

where Δf=f(t₂)−f(t₁), and where the constant lateral and verticalacceleration terms in the vehicle frame 20 as well as the bias terms arecanceled out in the subtraction.

Linearizing equation (14) in the neighborhood of (α,β,γ)=(0,0,0) yields:

$\begin{matrix}{\quad\{ {\begin{matrix}{{\Delta \; f_{x}^{b}} = {\Delta \; f_{y}^{v}}} \\{{\Delta \; f_{y}^{b}} = {{- {\gamma\Delta}}\; f_{x}^{v}}} \\{{\Delta \; f_{z}^{b}} = {\beta \; \Delta \; f_{x}^{v}}}\end{matrix},} } & (15)\end{matrix}$

which results in:

$\begin{matrix}{{\beta = \frac{\Delta \; f_{z}^{b}}{\Delta \; f_{x}^{v}}},} & (16) \\{\gamma = {- {\frac{\Delta \; f_{y}^{b}}{\Delta \; f_{x}^{v}}.}}} & (17)\end{matrix}$

If β≠0 or γ≠0, then the misalignment angle α is found by substitutingequations (16) and (17) into equation (14) as:

$\begin{matrix}{{\alpha = {\sin^{- 1}( \frac{{c_{1}\Delta \; f_{y}^{b}} + {c_{2}\Delta \; f_{z}^{b}}}{c_{1}^{2} + c_{2}^{2}} )}},} & (18)\end{matrix}$

where:

c ₁=sin β cos γΔf _(x) ^(v),  (19)

c ₂=sin γf _(x) ^(v).  (20)

The second case varies the longitudinal and vertical accelerations witha constant lateral acceleration. In this case, the following conditionsare provided.

f _(c) ^(v)(t ₁)≠f _(x) ^(v)(t ₂),  (21)

f _(y) ^(v)(t ₁)=f _(y) ^(v)(t ₂),  (22)

f _(z) ^(v)(t ₁)≠f _(z) ^(v)(t ₂).  (23)

Assuming that the bias terms remain constant at times t₁ and t₂ andusing equations (8), (9), (21), (22) and (23), the difference betweenthe actual IMU measurements at times t₁ and t₂ is found as:

$\begin{matrix}\{ {\begin{matrix}{{\Delta \; f_{x}^{b}} = {{\cos \; {\beta cos}\; {\gamma\Delta}\; f_{x}^{v}} - {\sin \; \beta \; f_{z}^{b}}}} \\{{\Delta \; f_{y}^{b}} = {{( {{\cos \; \gamma \; \sin \; {\alpha sin}\; \beta} - {\cos \; {\alpha sin}\; \gamma}} )\Delta \; f_{x}^{v}} + {\sin \; {\alpha cos}\; \beta \; f_{z}^{v}}}} \\{{\Delta \; f_{z}^{b}} = {{( {{\cos \; {\gamma cos}\; {\alpha sin\beta}} + {\sin \; {\alpha sin\gamma}}} )\Delta \; f_{x}^{v}} + {\cos \; {\alpha cos\beta\Delta}\; f_{z}^{v}}}}\end{matrix},}  & (24)\end{matrix}$

where the constant lateral acceleration terms and the bias terms in thevehicle frame 20 are cancelled out in the subtraction.

Linearizing equation (24) in the neighborhood of (α,β,γ)=(0,0,0) yields:

$\begin{matrix}\{ {\begin{matrix}{{\Delta \; f_{x}^{b}} = {{\Delta \; f_{x}^{v}} - {{\beta\Delta}\; f_{z}^{v}}}} \\{{\Delta \; f_{y}^{b}} = {{{- \gamma}\; \Delta \; f_{x}^{v}} + {\alpha \; \Delta \; f_{z}^{v}}}} \\{{\Delta \; f_{z}^{b}} = {{\beta \; \Delta \; f_{x}^{v}} + {\Delta \; f_{z}^{v}}}}\end{matrix}.}  & (25)\end{matrix}$

The misalignment angle is found from equation (25) as:

$\begin{matrix}{\beta = {\frac{{\Delta \; f_{z}^{b}} - {\Delta \; f_{z}^{v}}}{\Delta \; f_{x}^{v}}.}} & (26)\end{matrix}$

If an estimation of the misalignment angle γ is available from previoustimes, then:

$\begin{matrix}{\alpha = {\frac{{\Delta \; f_{y}^{b}} - {\Delta \; f_{x}^{v}}}{\Delta \; f_{z}^{v}}.}} & (27)\end{matrix}$

For implementing the misalignment estimation algorithm, the ideal IMUmeasurements in equations (5) and (6) are buffered in the module 32 fora short period of time (e.g., 1 second). The time t₂ is put equal to thecurrent time, and time t₁ is found by searching the buffered data, whichsatisfy the conditions in the first case or the second case. Since themisalignment angles are constant, they are updated as the average of thecurrent and previous values at each time instant.

Once the misalignment angles α, β and γ are calculated, the biascomponents for the accelerometers and gyros can be obtained usingequations (8), (9) and (10) as:

f _(bias) =f ^(b) −R _(v) ^(b) f ^(v),  (28)

ω_(bias)=ω^(b) −R _(v) ^(b)ω^(v).  (29)

FIG. 3 is a flowchart diagram 60 showing a process for providing the IMUerror estimation calculations in the boxes 38 and 40 as discussed above.Vehicle attitude measurements of roll φ, pitch θ and yaw ψ are providedat box 62 from the GNSS/INS data to box 64 that transforms the vehicleattitude measurements from the inertial frame to the vehicle frame 20 toobtain the transformation matrixes R_(n) ^(v) and {dot over (R)}_(n)^(v) as provided by equations (2) and (3). The transformation matrixesR_(n) ^(v) and {dot over (R)}_(n) ^(v) are then sent to box 66 toprovide compensated rates in the vehicle frame 20 including the angularrates ω^(v) and the skew-symmetric matrix Ω^(v) provided by equation(5). The vehicle velocity and acceleration values V^(v) and {dot over(V)}^(v) from the GNSS/INS data are provided at box 68 and arecompensated for acceleration in the vehicle frame 20 at box 70 using theskew-symmetric matrix Ω^(v) provided by equation (5) from the box 66 toobtain the measured acceleration vector f^(v) in the vehicle frame 20provided by equation (6). The algorithm uses the vector f^(v) from thebox 70 and the IMU acceleration measurement values f^(b) from box 74 tocalculate the misalignment angles α, β and γ using either the first casethrough equations (16), (17) and (18) or the second case throughequations (26) and (27). The algorithm generates the transformationmatrix R_(v) ^(b) for transformation from the vehicle frame 20 to thebody frame 22 using equation (10) at box 76. The transformation matrixR_(v) ^(b) is then provided to box 78 along with the angle rates ω^(b)in the body frame 22 from the IMU 16 at box 80 and the accelerationrates in the body frame 22 f^(b) from the box 74 to generate the biascomponents f_(bias) and ω_(bias) using equations (28) and (29), wherethe results obtained from equations (16), (17) and (18) or equations(26) and (27), and equations (28) and (29) give the nine values.

The nine values identifying the bias and misalignment errors from thebox 40 are provided to the module 34 so that if the GNSS/INS data is notavailable at any particular point in time, then the previous calculatederrors can be used to identify the vehicle velocity and attitude in casethose values are needed if the vehicle velocity sensors fail. Therefore,if the IMU data is needed to estimate the vehicle velocity as in the'894 application, those values are adjusted for the errors prior tobeing used for those calculations. The errors from the box 40 areapplied to an IMU bias and misalignment compensation box 46 in themodule 34 along with IMU angle rates ω^(b) at box 48 and the IMUacceleration values f^(b) at box 50 to be corrected as follows.

f ^(v)=(R _(v) ^(b))^(T)(f ^(b) −f _(bias)),  (30)

ω^(v)=(R _(v) ^(b))^(T)(ω^(b)−ω_(bias)).  (31)

Once the IMU angle rates and acceleration values are corrected at thebox 46, those corrected values can be used to determine the vehiclevelocity and attitude at box 52 as described, for example, in the '894application.

As will be well understood by those skilled in the art, the several andvarious steps and processes discussed herein to describe the inventionmay be referring to operations performed by a computer, a processor orother electronic calculating device that manipulate and/or transformdata using electrical phenomenon. Those computers and electronic devicesmay employ various volatile and/or non-volatile memories includingnon-transitory computer-readable medium with an executable programstored thereon including various code or executable instructions able tobe performed by the computer or processor, where the memory and/orcomputer-readable medium may include all forms and types of memory andother computer-readable media.

The foregoing discussion discloses and describes merely exemplaryembodiments of the present invention. One skilled in the art willreadily recognize from such discussion and from the accompanyingdrawings and claims that various changes, modifications and variationscan be made therein without departing from the spirit and scope of theinvention as defined in the following claims.

1. A method for correcting bias and angle misalignment errors in anglerate and acceleration outputs from a six-degree of freedom (DOF)inertial measurement unit (IMU), said IMU being mounted on a platform,said method comprising: obtaining velocity and estimation attitude datain an inertial frame; determining an acceleration estimation and a rateestimation in a platform frame using the velocity and attitude data; anddetermining an IMU bias error and a misalignment error using theacceleration and rate estimations and the angle rate and accelerationoutputs from the IMU.
 2. The method according to claim 1 whereinobtaining velocity and attitude data includes obtaining velocity andattitude data from a global navigation satellite system (GNSS) and/or aninertial navigation system (INS).
 3. The method according to claim 1wherein determining an acceleration estimation and a rate estimationincludes transforming velocity, roll angle, pitch angle and yaw angle ofthe platform in the inertial frame to the platform frame.
 4. The methodaccording to claim 3 wherein transforming velocity and roll, pitch andyaw includes using the equations:{dot over (V)} ^(v)=−Ω^(v) V ^(v) +R _(n) ^(v) g ^(n) +f ^(v){dot over (R)} _(n) ^(v)=−Ω^(v) R _(n) ^(v) where {dot over (V)}^(v) isa time derivate of the velocity in the platform frame,g^(n)=[0,0,−9.80665]^(t) is a vector of gravitation acceleration in theinertial frame, f^(v) is an acceleration vector in the platform frame,and R_(n) ^(v) is a transformation matrix from the inertial frame to theplatform frame corresponding to angle rotations of roll φ, pitch θ andyaw ψ where: $R_{n}^{v} = {\quad\begin{bmatrix}{\cos \; {\theta cos\psi}} & {\cos \; {\theta sin\psi}} & {{- \sin}\; \theta} \\{{\cos \; {\psi sin\varphi sin}\; \theta} - {\cos \; {\varphi sin}\; \psi}} & {{\sin \; {\varphi sin}\; {\theta sin}\; \psi} + {\cos \; \varphi \; \cos \; \psi}} & {\cos \; {\theta sin\varphi}} \\{{\cos \; {\psi cos\varphi cos\theta}} + {\sin \; {\varphi sin\psi}}} & {{\cos \; {\varphi sin}\; {\theta sin\psi}} - {\sin \; {\varphi cos}\; \psi}} & {\cos \; {\theta cos}\; \varphi}\end{bmatrix}}$ and where Ω^(v)=[ω^(v)x] is a skew-symmetric matrix ofthe IMU angular rate outputs as: $\Omega^{v} = \begin{bmatrix}0 & {- \omega_{\psi}} & \omega_{\theta} \\\omega_{\psi} & 0 & {- \omega_{\varphi}} \\{- \omega_{\theta}} & \omega_{\varphi} & 0\end{bmatrix}$$\Omega^{v} = {{\overset{.}{R}}_{n}^{v}( R_{n}^{v} )}^{T}$$f^{v} = {{\overset{.}{V}}^{v} + {\Omega^{v}V^{v}} - {R_{n}^{v}g^{n}}}$where ω is the angular rate, {dot over (V)}^(v) is obtained usingnumerical derivatives of the velocity data, and {dot over (R)}_(n) ^(v)is obtained as:${\overset{.}{R}}_{n}^{v} = {{\frac{\partial R_{n}^{v}}{\varphi}\overset{.}{\varphi}} + {\frac{\partial R_{n}^{v}}{\theta}\overset{.}{\theta}} + {\frac{\partial R_{n}^{v}}{\psi}\overset{.}{\psi}}}$where {dot over (φ)}, {dot over (θ)} and {dot over (ψ)} are calculatednumerically.
 5. The method according to claim 1 wherein determining anIMU bias error and a misalignment error includes comparing theacceleration estimation and the rate estimation to the angle rate andacceleration outputs from the IMU.
 6. The method according to claim 5wherein comparing the acceleration estimation and the rate estimationincludes transforming the acceleration and angular rate estimations fromthe platform frame to an IMU body frame.
 7. The method according toclaim 6 wherein determining the IMU bias error and misalignment errorincludes identifying three misalignment angles using the accelerationestimations at two different sample time.
 8. The method according toclaim 7 wherein determining the misalignment angles includes varying alongitudinal acceleration of the platform with a constant lateral andvertical acceleration.
 9. The method according to claim 7 whereindetermining the misalignment angles includes varying the longitudinaland vertical accelerations of the platform with a constant lateralacceleration.
 10. The method according to claim 7 wherein determiningthe IMU bias error and misalignment error includes using equations:f _(bias) =f ^(b) −R _(v) ^(b) f ^(v),ω_(bias)=ω^(b) −R _(v) ^(b)ω^(v).
 11. The method according to claim 1further comprising using the IMU bias error and misalignment error todetermine platform velocity in the event that providing velocity andestimation attitude data is unavailable.
 12. The method according toclaim 1 wherein the platform is a vehicle and the IMU is mounted to avehicle structure.
 13. A method for correcting bias and anglemisalignment errors in angle rate and acceleration outputs from asix-degree of freedom (DOF) inertial measurement unit (IMU) mounted on avehicle, said method comprising: providing velocity and estimationattitude data in an inertial frame from a global navigation satellitesystem (GNSS) and/or an inertial navigation system (INS); determining anacceleration estimation and a rate estimation in a vehicle frame usingthe velocity and attitude data that includes transforming velocity, rollangle, pitch angle and yaw angle of the vehicle in the inertial frame tothe vehicle frame; and determining an IMU bias error and a misalignmenterror using the acceleration and rate estimations and the angle rate andacceleration outputs from the IMU that includes comparing theacceleration estimation and the rate estimation to the angle rate andacceleration outputs from the IMU and transforming the acceleration andangular rate estimations from the vehicle frame to an IMU body frame.14. The method according to claim 13 wherein determining the IMU biaserror and misalignment error includes identifying three misalignmentangles using the acceleration estimations at two different sample time.15. The method according to claim 14 wherein determining themisalignment angles includes varying a longitudinal acceleration of thevehicle with a constant lateral and vertical acceleration.
 16. Themethod according to claim 14 wherein determining the misalignment anglesincludes varying the longitudinal and vertical accelerations of thevehicle with a constant lateral acceleration.
 17. The method accordingto claim 14 wherein determining the IMU bias error and misalignmenterror includes using equations:f _(bias) =f ^(b) −R _(v) ^(b) f ^(v),ω_(bias)=ω^(b) −R _(v) ^(b)ω^(v), where f_(bias) is the misalignmenterror, f^(b) is acceleration in the body frame, f^(v) is acceleration inthe vehicle frame, ω_(bias) is the bias error, ω^(b) is angular rate inthe body frame, ω^(v) is angular rate in the vehicle frame, and R_(v)^(b) is a transformational matrix between the vehicle frame and the bodyframe.
 18. The method according to claim 13 further comprising using theIMU bias error and misalignment error to determine vehicle velocity inthe event that providing velocity and estimation attitude data isunavailable.
 19. A system for correcting bias and angle misalignmenterrors in angle rate and acceleration outputs from a six-degree offreedom (DOF) inertial measurement unit (IMU), said IMU being mounted ona platform, said system comprising: means for providing velocity andestimation attitude data in an inertial frame; means for determining anacceleration estimation and a rate estimation in a platform frame usingthe velocity and attitude data; and means for determining an IMU biaserror and a misalignment error using the acceleration and rateestimations and the angle rate and acceleration outputs from the IMU.20. The system according to claim 19 wherein the means for providingvelocity and attitude data provides the velocity and attitude data froma global navigation satellite system (GNSS) and/or an inertialnavigation system (INS).